In our experiments, we use the scheduling of parameter \(\lambda \)  as in [1]}. The baseline training procedure is extended to sample from the unlabeled data. At every iteration, we additionally sample 16 pairs of axial slices (the batch size is 32) from one of the Paired-private or Paired-public datasets (depending on a data setup). Then, we make the second forward pass to the discriminator and sum the segmentation and adversarial losses. The rest of the pipeline remains the same as in the baseline.
